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We introduce a framework for quantifying semantic variation of common words in Communities of Practice and in sets of topic-related communities. We show that while some meaning shifts are shared across related communities, others are…

The established language for statistical testing --- significance levels, power, and p-values --- is overly complicated and deceptively conclusive. Even teachers of statistics and scientists who use statistics misinterpret the results of…

The use of causal language in observational studies has raised concerns about overstatement in scientific communication. While some argue that such language should be reserved for randomized controlled trials, others contend that rigorous…

The rapid rise of large language models (LLMs) is reshaping the landscape of automatic assessment in education. While these systems demonstrate substantial advantages in adaptability to diverse question types and flexibility in output…

Citations in science are being studied from several perspectives, among which approaches such as scientometrics and science of science. In this chapter I briefly review some of the literature on citations, citation distributions and models…

Many data mining approaches aim at modelling and predicting human behaviour. An important quantity of interest is the quality of model-based predictions, e.g. for finding a competition winner with best prediction performance. In real life,…

The low replication rate of published studies has long concerned the social science community, making understanding the replicability a critical problem. Several studies have shown that relevant research communities can make predictions…

In the evaluation of scientific publications' impact, the interplay between intrinsic quality and non-scientific factors remains a subject of debate. While peer review traditionally assesses quality, bibliometric techniques gauge scholarly…

The peer review process is often regarded as the gatekeeper of scientific integrity, yet increasing evidence suggests that it is not immune to bias. Although structural inequities in peer review have been widely debated, much less attention…

Predictive uncertainty estimation of pre-trained language models is an important measure of how likely people can trust their predictions. However, little is known about what makes a model prediction uncertain. Explaining predictive…

The citation impact of a scientific publication is usually seen as a one-dimensional concept. We introduce a multi-dimensional framework for characterizing the citation impact of a publication. In addition to the level of citation impact,…

Spreadsheet users regularly deal with uncertainty in their data, for example due to errors and estimates. While an insight into data uncertainty can help in making better informed decisions, prior research suggests that people often use…

Algorithmic transparency entails exposing system properties to various stakeholders for purposes that include understanding, improving, and contesting predictions. Until now, most research into algorithmic transparency has predominantly…

It is usual to consider that standards generate mixed feelings among scientists. They are often seen as not really reflecting the state of the art in a given domain and a hindrance to scientific creativity. Still, scientists should…

One of the most crucial issues in data mining is to model human behaviour in order to provide personalisation, adaptation and recommendation. This usually involves implicit or explicit knowledge, either by observing user interactions, or by…

Measuring research impact is important for ranking publications in academic search engines and for research evaluation. Social media metrics or altmetrics measure the impact of scientific work based on social media activity. Altmetrics are…

Reproducibility is an important feature of science; experiments are retested, and analyses are repeated. Trust in the findings increases when consistent results are achieved. Despite the importance of reproducibility, significant work is…

While the modern science is characterized by an exponential growth in scientific literature, the increase in publication volume clearly does not reflect the expansion of the cognitive boundaries of science. Nevertheless, most of the metrics…

Linguistic uncertainty is common in social media, but its relationship with engagement remains unclear across languages and topics. Using 2,258 English-language posts on Federal Reserve policy, inflation, and electoral politics collected…

Susceptibility to misinformation describes the degree of belief in unverifiable claims, a latent aspect of individuals' mental processes that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs, which…